





'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class 					__snake_case  (					unittest.TestCase							):






   '''simple docstring'''





   lowerCamelCase__    							=      ViTImageProcessor if is_vision_available() else None

   @property
   def 		__UpperCamelCase						(							self       ):
    return self.image_processor_tester.prepare_image_processor_dict()

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[Any]    =  (3, 3_2, 1_2_8)
    snake_case__     :					List[Any]    =  tempfile.mkdtemp()

    # fmt: off
    snake_case__     :					str    =  ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
    # fmt: on
    snake_case__     :					List[str]    =  dict(zip(__SCREAMING_SNAKE_CASE	,		range(len(__SCREAMING_SNAKE_CASE       )       )       )       )

    snake_case__     :					Optional[Any]    =  os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES["""vocab_file"""]       )
    with open(self.vocab_file	,		"""w"""	,		encoding="""utf-8"""       ) as fp:
     fp.write(json.dumps(__SCREAMING_SNAKE_CASE       ) + """\n"""       )

    snake_case__     :					Optional[int]    =  {
        """do_normalize""": False,
        """do_resize""": True,
        """image_processor_type""": """ViTImageProcessor""",
        """resample""": 3,
        """size""": {"""height""": 3_2, """width""": 1_2_8},
    }
    snake_case__     :					str    =  os.path.join(self.tmpdirname	,		__SCREAMING_SNAKE_CASE       )
    with open(self.image_processor_file	,		"""w"""	,		encoding="""utf-8"""       ) as fp:
     json.dump(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    shutil.rmtree(self.tmpdirname       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[Any]    =  np.random.randint(2_5_5	,		size=(3, 3_0, 4_0_0)	,		dtype=np.uinta       )

    snake_case__     :					List[str]    =  Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE	,		0	,		-1       )       )

    return image_input

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[str]    =  self.get_tokenizer()
    snake_case__     :					Union[str, Any]    =  self.get_image_processor()

    snake_case__     :					Optional[int]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )
    processor.save_pretrained(self.tmpdirname       )
    snake_case__     :					Tuple    =  MgpstrProcessor.from_pretrained(self.tmpdirname	,		use_fast=__SCREAMING_SNAKE_CASE       )

    self.assertEqual(processor.char_tokenizer.get_vocab()	,		tokenizer.get_vocab()       )
    self.assertIsInstance(processor.char_tokenizer	,		__SCREAMING_SNAKE_CASE       )

    self.assertEqual(processor.image_processor.to_json_string()	,		image_processor.to_json_string()       )
    self.assertIsInstance(processor.image_processor	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					int    =  self.get_tokenizer()
    snake_case__     :					Union[str, Any]    =  self.get_image_processor()

    snake_case__     :					Optional[int]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )
    processor.save_pretrained(self.tmpdirname       )

    snake_case__     :					str    =  self.get_tokenizer(bos_token="""(BOS)"""	,		eos_token="""(EOS)"""       )
    snake_case__     :					List[Any]    =  self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE	,		padding_value=1.0       )

    snake_case__     :					Optional[int]    =  MgpstrProcessor.from_pretrained(
        self.tmpdirname	,		bos_token="""(BOS)"""	,		eos_token="""(EOS)"""	,		do_normalize=__SCREAMING_SNAKE_CASE	,		padding_value=1.0       )

    self.assertEqual(processor.char_tokenizer.get_vocab()	,		tokenizer_add_kwargs.get_vocab()       )
    self.assertIsInstance(processor.char_tokenizer	,		__SCREAMING_SNAKE_CASE       )

    self.assertEqual(processor.image_processor.to_json_string()	,		image_processor_add_kwargs.to_json_string()       )
    self.assertIsInstance(processor.image_processor	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[int]    =  self.get_image_processor()
    snake_case__     :					Tuple    =  self.get_tokenizer()

    snake_case__     :					Optional[Any]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Dict    =  self.prepare_image_inputs()

    snake_case__     :					Tuple    =  image_processor(__SCREAMING_SNAKE_CASE	,		return_tensors="""np"""       )
    snake_case__     :					Optional[int]    =  processor(images=__SCREAMING_SNAKE_CASE	,		return_tensors="""np"""       )

    for key in input_image_proc.keys():
     self.assertAlmostEqual(input_image_proc[key].sum()	,		input_processor[key].sum()	,		delta=1e-2       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Dict    =  self.get_image_processor()
    snake_case__     :					Union[str, Any]    =  self.get_tokenizer()

    snake_case__     :					int    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Any    =  """test"""

    snake_case__     :					Optional[Any]    =  processor(text=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Any    =  tokenizer(__SCREAMING_SNAKE_CASE       )
    for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]	,		encoded_processor[key]       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Union[str, Any]    =  self.get_image_processor()
    snake_case__     :					List[Any]    =  self.get_tokenizer()

    snake_case__     :					Any    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					int    =  """test"""
    snake_case__     :					Any    =  self.prepare_image_inputs()

    snake_case__     :					Union[str, Any]    =  processor(text=__SCREAMING_SNAKE_CASE	,		images=__SCREAMING_SNAKE_CASE       )

    self.assertListEqual(list(inputs.keys()       )	,		["""pixel_values""", """labels"""]       )

    # test if it raises when no input is passed
    with pytest.raises(__SCREAMING_SNAKE_CASE       ):
     processor()

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[str]    =  self.get_image_processor()
    snake_case__     :					Optional[Any]    =  self.get_tokenizer()

    snake_case__     :					List[str]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					int    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    snake_case__     :					Any    =  processor.char_decode(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Tuple    =  tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Optional[int]    =  [seq.replace(""" """	,		""""""       ) for seq in decoded_tok]

    self.assertListEqual(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[str]    =  self.get_image_processor()
    snake_case__     :					str    =  self.get_tokenizer()

    snake_case__     :					Optional[int]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					int    =  None
    snake_case__     :					Optional[int]    =  self.prepare_image_inputs()

    snake_case__     :					List[Any]    =  processor(text=__SCREAMING_SNAKE_CASE	,		images=__SCREAMING_SNAKE_CASE       )

    self.assertListEqual(list(inputs.keys()       )	,		processor.model_input_names       )


   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[Any]    =  self.get_image_processor()
    snake_case__     :					List[Any]    =  self.get_tokenizer()

    snake_case__     :					Optional[Any]    =  MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					str    =  torch.randn(1	,		2_7	,		3_8       )
    snake_case__     :					Dict    =  torch.randn(1	,		2_7	,		5_0_2_5_7       )
    snake_case__     :					Union[str, Any]    =  torch.randn(1	,		2_7	,		3_0_5_2_2       )

    snake_case__     :					List[str]    =  processor.batch_decode([char_input, bpe_input, wp_input]       )

    self.assertListEqual(list(results.keys()       )	,		["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""]       )





"""simple docstring"""


import random





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   dict:




						'''simple docstring'''



						lowerCamelCase__   ={i: [] for i in range(__lowerCAmelCase							)}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(__lowerCAmelCase							)
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(__lowerCAmelCase							):
												for j in range(i + 1  ,       __lowerCAmelCase							):
																		if random.random() < probability:
																								graph[i].append(__lowerCAmelCase							)
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(__lowerCAmelCase							)
						return graph





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   dict:




						'''simple docstring'''



						return {
						    i: [j for j in range(__lowerCAmelCase							) if i != j] for i in range(__lowerCAmelCase							)
						}


if __name__ == "__main__":
	import doctest

	doctest.testmod()


